<title>Semiautomatic classification of alveolar bone quality</title>

Abstract

A semiautomated, radiograph-based classifier of alveolar bone quality for dry skulls was developed Bone quality was based on the assessment of surface features, such as the resorption of cortical bone and the presence of vertical defects. The consensus of two trained observers was used to rate 50 mandibularquadrants of 29 skulls as having normal or poor alveolar bone quality. Bitewing radiographs were taken of the mandibles and digitized with a 35-mm, solid-state slide scanner at 1024 x 1520 x 8 bits. Regions of interest (ROl) of alveolar bone between the mandibular first and second molars were chosen. For these ROTs, Gray-scale values were plotted as histograms. Nonzero portions of the histogram were mapped to a 100-cell scale and cumulative percentage frequency curves of these were calculated. Average cumulative frequency distributions were calculated for 14 cases with normal bone quality and 1 1 cases with poor bone quality. These distributions were used to develop an automatic classifier based on differences between the cumulative frequency curve for each case and the average cumulative frequency curves for normal and poor quality bone. The bone quality of 43 of the 50quadrantswas successfully determined with this classifier. Of the seven misses, two were from one skull with severely tilted teeth; three were associated with bleached museum specimens; and the remaining two appeared to be a failure of the classifier. These preliminary results are encouraging. This classifier will be applied to a longitudinal series of bitewings of patients to predict alveolar bone loss.